Imputing Missing Values with External Data

TL;DR

This paper introduces a new method and a Stata command for imputing missing data using external study information, enabling data sharing without compromising individual data privacy.

Contribution

The paper presents a novel imputation approach that leverages external data and covariance structures, implemented in a user-friendly Stata command.

Findings

Enables imputation without sharing individual data

Uses linear predictors and covariance matrices from external models

Facilitates collaborative research with missing data

Abstract

Missing data is a common challenge across scientific disciplines. Current imputation methods require the availability of individual data to impute missing values. Often, however, missingness requires using external data for the imputation. In this paper, we introduce a new Stata command, mi impute from, designed to impute missing values using linear predictors and their related covariance matrix from imputation models estimated in one or multiple external studies. This allows for the imputation of any missing values without sharing individual data between studies. We describe the underlying method and present the syntax of mi impute from alongside practical examples of missing data in collaborative research projects.